def print_param():
p_base = load_param(get_bert_full_path())
nli_path = "C:\work\Code\Chair\output\model\\runs\\nli_model.ckpt-75000_NLI\\model-0"
p_ft = load_param(nli_path)
keys = list(p_base.keys())
key = "bert/encoder/layer_0/output/dense/kernel"
param1 = p_base[key]
param2 = p_ft[key]
html = HtmlVisualizer("bert_dense_param.html")
l , c = param1.shape
s_score = 100
for i in range(l):
rows = []
row1 = []
row2 = []
s_score = 100 - s_score
score = s_score
for j in range(c):
score = 100 - score
row1.append(Cell("{0:.4f}".format(param1[i, j]), score))
row2.append(Cell("{0:.4f}".format(param2[i, j]), score))
rows.append(row1)
rows.append(row2)
html.write_table(rows)
def main(config):
# select claims
# load relevant documents
# remove duplicate
q_res_path = config['q_res_path']
ranked_list: Dict[
str, List[SimpleRankedListEntry]] = load_galago_ranked_list(q_res_path)
claims = get_all_claims()
claim_d = claims_to_dict(claims)
keys = list(ranked_list.keys())
keys.sort()
num_doc_per_query = 10
url_prefix = "http://localhost:36559/document?identifier="
rows = []
for query_id in keys[:10]:
entries: List[SimpleRankedListEntry] = ranked_list[query_id]
entries = entries[:num_doc_per_query * 3]
doc_ids: List[str] = remove_duplicate(list([e.doc_id
for e in entries]))
claim = claim_d[int(query_id)]
s = "{} : {}".format(query_id, claim)
rows.append([Cell(s)])
for doc_id in doc_ids[:num_doc_per_query]:
url = url_prefix + doc_id
s = "<a href=\"{}\">{}</a>".format(url, doc_id)
rows.append([Cell(s)])
html = HtmlVisualizer("claim_docs_urls.html")
html.write_table(rows)
def view_grad_overlap_hidden():
filename = "ukp_feature_overlap.pickle"
obj = pickle.load(open(os.path.join(output_path, filename), "rb"))
out_name = filename.split(".")[0] + ".html"
html_writer = HtmlVisualizer(out_name, dark_mode=False)
data = EstimatorPredictionViewerGosford(filename)
for inst_i, entry in enumerate(data):
tokens = entry.get_mask_resolved_input_mask_with_input()
h_overlap = entry.get_vector('h_overlap')
std = np.std(h_overlap, axis=2)
mean = np.mean(h_overlap, axis=2)
h_overlap = np.sum(h_overlap, axis=2)
highlight = lmap(is_mask, tokens)
cells = data.cells_from_tokens(tokens, highlight)
rows = [cells]
for layer_i in range(12):
e = h_overlap[layer_i, :]
e = [v * 1e6 for v in e]
cells = data.cells_from_scores(e)
rows.append(cells)
e = [v * 1e8 for v in std[layer_i, :]]
cells2 = data.cells_from_scores(e)
rows.append(cells2)
print(entry.get_vector("masked_lm_example_loss"))
html_writer.multirow_print_from_cells_list(rows, 40)
def main(config):
# select claims
# load relevant documents
# remove duplicate
q_res_path = config['q_res_path']
ranked_list: Dict[
str, List[SimpleRankedListEntry]] = load_galago_ranked_list(q_res_path)
query_text_d = json.load(open(config['query_text_d']))
save_name = config['save_path']
keys = list(ranked_list.keys())
keys.sort()
num_doc_per_query = 10
url_prefix = "http://localhost:36559/document?identifier="
rows = []
for query_id in keys[:100]:
entries: List[SimpleRankedListEntry] = ranked_list[query_id]
entries = entries[:num_doc_per_query * 3]
doc_ids: List[str] = list([e.doc_id for e in entries])
query_text = query_text_d[query_id]
s = "{} : {}".format(query_id, query_text)
rows.append([Cell(s)])
for doc_id in doc_ids[:num_doc_per_query]:
url = url_prefix + doc_id
s = "<a href=\"{}\">{}</a>".format(url, doc_id)
rows.append([Cell(s)])
html = HtmlVisualizer(save_name)
html.write_table(rows)
def show(html_visualizer: HtmlVisualizer,
features: List[ParagraphClaimPersFeature]):
print("Cid: ", features[0].claim_pers.cid)
for f in features:
html_visualizer.write_paragraph("Claim: " + f.claim_pers.claim_text)
html_visualizer.write_paragraph("Perspective: " + f.claim_pers.p_text)
pc_tokens: List[str] = nltk.word_tokenize(
f.claim_pers.claim_text) + nltk.word_tokenize(f.claim_pers.p_text)
pc_tokens_set = set([t.lower() for t in pc_tokens])
print(pc_tokens_set)
def get_cell(token) -> Cell:
if token.lower() in pc_tokens_set:
score = 100
else:
score = 0
return Cell(token, score)
html_visualizer.write_paragraph("Label : {}".format(
f.claim_pers.label))
for score_paragraph in f.feature:
paragraph = score_paragraph.paragraph
cells = [get_cell(t) for t in paragraph.tokens]
html_visualizer.write_paragraph("---")
html_visualizer.multirow_print(cells, width=20)
def main():
html = HtmlVisualizer("tooltip_test.html",
dark_mode=False,
use_tooltip=True)
line = [("1", "hello"), ("2", "word")]
html.write_span_line(line)
html.write_span_line(line)
def visualize_prediction_data(data_id):
tokenizer = get_tokenizer()
num_samples_list = open(
os.path.join(working_path, "entry_prediction_n", data_id),
"r").readlines()
p = os.path.join(working_path, "entry_loss",
"entry{}.pickle".format(data_id))
loss_outputs_list = pickle.load(open(p, "rb"))
print("Loaded input data")
loss_outputs = []
for e in loss_outputs_list:
loss_outputs.extend(e["masked_lm_example_loss"])
print("Total of {} loss outputs".format(len(loss_outputs)))
instance_idx = 0
feature_itr = load_record_v2(
os.path.join(working_path, "entry_prediction_tf.done", data_id))
n = len(num_samples_list)
n = 100
html = HtmlVisualizer("entry_prediction.html")
for i in range(n):
n_sample = int(num_samples_list[i])
assert n_sample > 0
first_inst = feature_itr.__next__()
feature = Feature2Text(first_inst, tokenizer)
html.write_headline("Input:")
html.write_paragraph(feature.get_input_as_text(True, True))
html.write_headline("Word:" + feature.get_selected_word_text())
if instance_idx + n_sample >= len(loss_outputs):
break
if n_sample == 1:
continue
rows = []
no_dict_loss = loss_outputs[instance_idx]
row = [Cell(no_dict_loss, 0), Cell("")]
rows.append(row)
instance_idx += 1
for j in range(1, n_sample):
feature = Feature2Text(feature_itr.__next__(), tokenizer)
def_cell = Cell(feature.get_def_as_text())
loss = loss_outputs[instance_idx]
hl_score = 100 if loss < no_dict_loss * 0.9 else 0
row = [Cell(loss, hl_score), def_cell]
rows.append(row)
instance_idx += 1
html.write_table(rows)
def loss_view(dir_path):
tokenizer = get_tokenizer()
html_writer = HtmlVisualizer("ukp_lm_grad_high.html", dark_mode=False)
for file_path in get_dir_files(dir_path):
items = pickle.load(open(file_path, "rb"))
for e in items:
input_ids, masked_input_ids, masked_lm_example_loss = e
tokens = mask_resolve_1(
tokenizer.convert_ids_to_tokens(input_ids),
tokenizer.convert_ids_to_tokens(masked_input_ids))
highlight = lmap(is_mask, tokens)
cells = cells_from_tokens(tokens, highlight)
html_writer.multirow_print(cells)
def main():
save_name = sys.argv[1]
out_dir = os.path.join(output_path, "cppnc")
exist_or_mkdir(out_dir)
info_file_path = os.path.join(out_dir, "cppnc_triple_all_dev_info")
pred_file_path = os.path.join(out_dir, save_name + ".score")
cid_and_confidences = get_confidence_list_per_cid(info_file_path, pred_file_path)
rows = []
for cid, confidenc_list in cid_and_confidences.items():
row = list()
row.append(Cell(str(cid)))
row.extend([Cell("", highlight_score=c*100) for c in confidenc_list])
rows.append(row)
html = HtmlVisualizer("confidence.html")
html.write_table(rows)
def main():
file_path = sys.argv[1]
name = os.path.basename(file_path)
viewer = EstimatorPredictionViewer(file_path)
html = HtmlVisualizer("toke_score_gold.html")
stopwords = load_stopwords_for_query()
skip = 10
for entry_idx, entry in enumerate(viewer):
if entry_idx % skip != 0:
continue
tokens = entry.get_tokens("input_ids")
input_ids = entry.get_vector("input_ids")
label_ids = entry.get_vector("label_ids")
label_ids = np.reshape(label_ids, [-1, 2])
log_label_ids = np.log(label_ids + 1e-10)
seg1, seg2 = split_p_h_with_input_ids(tokens, input_ids)
pad_idx = tokens.index("[PAD]")
assert pad_idx > 0
logits = entry.get_vector("logits")
cells = []
cells2 = []
for idx in range(pad_idx):
probs = label_ids[idx]
token = tokens[idx]
score = probs[0]
color = "B" if score > 0 else "R"
highlight_score = min(abs(score) * 10000, 100)
if token in stopwords:
highlight_score = 0
if token in seg1:
highlight_score = 50
color = "G"
c = Cell(token,
highlight_score=highlight_score,
target_color=color)
cells.append(c)
html.multirow_print_from_cells_list([cells, cells2])
if entry_idx > 10000:
break
def main():
first_list_path = sys.argv[1]
dir_path = sys.argv[2]
save_path = sys.argv[3]
l: Dict[str, List[TrecRankedListEntry]] = load_ranked_list_grouped(
first_list_path)
new_entries: Dict[str, List[TrecRankedListEntry]] = l
flat_entries: Iterable[TrecRankedListEntry] = flatten(new_entries.values())
html = HtmlVisualizer(save_path)
rows = []
for e in flat_entries:
ahref = "<a href=\"./{}/{}.html\">{}</a>".format(
dir_path, e.doc_id, e.doc_id)
row = lmap(Cell, [e.query_id, e.rank, e.score, ahref])
rows.append(row)
html.write_table(rows)
def print_paragraph_feature(pf_list: List[ParagraphFeature],
out_path: FilePath):
html = HtmlVisualizer(out_path)
for pf in pf_list:
html.write_paragraph("Text 1: " + pf.datapoint.text1)
html.write_paragraph("Text 2: " + pf.datapoint.text2)
for f in pf.feature:
s = " ".join(f.paragraph.tokens)
html.write_paragraph(s)
html.close()
def print_file(pred_path):
grouped = load_prediction(pred_path)
html_pos = HtmlVisualizer("pc_view_true.html")
html_neg = HtmlVisualizer("pc_view_false.html")
item_cnt = 0
for key in grouped:
paras: List[Tuple[str, float, Segment]] = grouped[key]
is_true_arr = list([t[1] > 0.5 for t in paras])
cnt_true = sum(is_true_arr)
if cnt_true == len(is_true_arr) or cnt_true == 0:
continue
cnt_false = len(is_true_arr) - cnt_true
idx_false = 0
idx_true = 0
item_cnt += 1
for _, score, tokens in paras:
is_true = score > 0.5
html = html_pos if is_true else html_neg
claim, perspective, paragraph = split_3segments(tokens)
highlight_terms = set(claim + perspective)
if is_true:
html.write_paragraph("{} of {}".format(idx_true, cnt_true))
idx_true += 1
else:
html.write_paragraph("{} of {}".format(idx_false, cnt_false))
idx_false += 1
html.write_paragraph("claim : " + pretty_tokens(claim))
html.write_paragraph("perspective : " + pretty_tokens(perspective))
def make_cell(subword: Subword):
if subword in highlight_terms:
return Cell(subword, highlight_score=100)
else:
return Cell(subword)
cells = lmap(make_cell, paragraph)
html.multirow_print(cells)
if item_cnt > 100:
break
def main():
#claim_d = load_train_claim_d()
html = HtmlVisualizer("doc_relevance_and_value.html")
rows = []
data_id = 0
for query, k_list in load_qk():
claim_id = query.query_id
claim_text = query.text
doc_ids = set([k.doc_id for k in k_list])
for doc_id in list(doc_ids)[:10]:
url = os.path.join(output_path, "pc_docs_html", doc_id + ".html")
a = "<a href=\"{}\">url</a>".format(url)
#tab_print(data_id, claim_id, doc_id)
row = [Cell(data_id), Cell(claim_id), Cell(claim_text), Cell(a)]
rows.append(row)
data_id += 1
html.write_table(rows)
def analyze_hv(hv_tt, hv_lm, tt_grad, tokenizer):
batch_size = 16
seq_len = 200
hidden_dim = 768
reshaped_grad = reshape_gradienet(tt_grad, seq_len, hidden_dim, False)
hv_tt, x_list = reshape(hv_tt)
hv_lm, x_list = reshape(hv_lm)
assert len(hv_lm) == len(hv_tt)
html = HtmlVisualizer("Preserved.html")
for inst_i in range(len(hv_lm)):
print("\t", end="")
tokens = tokenizer.convert_ids_to_tokens(x_list[inst_i])
for seq_i in range(seq_len):
token = tokenizer.convert_ids_to_tokens([x_list[inst_i, seq_i]])[0]
print("{}".format(token), end="\t")
print()
scores = []
for layer_i in range(13):
if layer_i != 1:
continue
layer_no = layer_i
if layer_no >= 1:
print("Layer {} :".format(layer_no), end="\t")
else:
print("Embedding:", end="\t")
for seq_i in range(seq_len):
n_diff_1, n_diff_2 = diff_and_grad(
hv_lm[inst_i, layer_i, seq_i], hv_tt[inst_i, layer_i,
seq_i],
reshaped_grad[inst_i, layer_i, seq_i])
scores.append(n_diff_1)
print("{}({})".format(n_diff_1, n_diff_2), end="\t")
print("\n")
row = []
for t, s in zip(tokens, scores):
score = s / hidden_dim * 100
row.append(Cell(t, score))
html.write_table([row])
print("-----------------")
def main():
first_list_path = sys.argv[1]
dir_path = sys.argv[2]
save_path = sys.argv[3]
l: Dict[str, List[TrecRankedListEntry]] = load_ranked_list_grouped(
first_list_path)
new_entries: Dict[str, List[TrecRankedListEntry]] = l
def get_html_path_fn(doc_id):
return os.path.join(dir_path, "{}.html".format(doc_id))
doc_id_to_url = load_from_pickle("urls_d")
flat_entries: Iterable[TrecRankedListEntry] = flatten(new_entries.values())
entries = [
enrich(e, get_html_path_fn, doc_id_to_url) for e in flat_entries
]
html = HtmlVisualizer(save_path,
additional_styles=[
get_link_highlight_code(),
get_bootstrap_include_source()
])
rows = []
head = [
get_table_head_cell("query"),
get_table_head_cell("rank"),
get_table_head_cell("score"),
get_table_head_cell("doc_id"),
get_table_head_cell("title", 300),
get_table_head_cell("url"),
]
for e in entries:
html_path = os.path.join(dir_path, "{}.html".format(e.doc_id))
ahref = "<a href=\"{}\" target=\"_blank\">{}</a>".format(
html_path, e.doc_id)
elem_list = [e.query_id, e.rank, e.score, ahref, e.title, e.url]
row = lmap(Cell, elem_list)
rows.append(row)
html.write_table_with_class(rows, "table")
def show(filename):
data = EstimatorPredictionViewerGosford(filename)
html_writer = HtmlVisualizer("token_scoring.html", dark_mode=False)
correctness = []
for entry in data:
tokens = entry.get_tokens("input_ids")
logits = entry.get_vector("logits")
masks = entry.get_vector("label_masks")
ids = entry.get_vector("labels")
token_row = []
pred_row = []
gold_row = []
rows = [token_row, pred_row, gold_row]
for idx, token in enumerate(tokens):
token_cell = Cell(token)
if token == "[PAD]":
break
model_score = logits[idx][0]
if masks[idx]:
correct = (model_score > 0 and ids[idx] > 0) or (model_score < 0 and ids[idx] < 0)
color = "B" if correct else "R"
if correct and (model_score > 0 and ids[idx] > 0) :
color = "G"
pred_cell = Cell("{0:.2f}".format(model_score), 100, target_color=color)
gold_cell = Cell("{0:.2f}".format(ids[idx]), 100, target_color=color)
else:
token_cell = Cell(token)
pred_cell = Cell("")
gold_cell = Cell("")
token_row.append(token_cell)
pred_row.append(pred_cell)
gold_row.append(gold_cell)
html_writer.multirow_print_from_cells_list(rows, 20)
def draw():
#name = "pc_para_D_grad"
name = "pc_para_I_grad"
#name = "pc_para_H_grad"
data = EstimatorPredictionViewerGosford(name)
html_writer = HtmlVisualizer(name + ".html", dark_mode=False)
for inst_i, entry in enumerate(data):
tokens = entry.get_tokens("input_ids")
grad = entry.get_vector("gradient")
m = min(grad)
cells = data.cells_from_tokens(tokens)
for i, cell in enumerate(cells):
cells[i].highlight_score = min(abs(grad[i]) * 1e4, 255)
cells[i].target_color = "B" if grad[i] > 0 else "R"
print(grad)
prob = softmax(entry.get_vector("logits"))
pred = np.argmax(prob)
label = entry.get_vector("labels")
html_writer.write_paragraph("Label={} / Pred={}".format(str(label), pred))
html_writer.multirow_print(cells)
def draw2(in_file, out_file):
filename = os.path.join(output_path, in_file)
data = EstimatorPredictionViewerGosford(filename)
html_writer = HtmlVisualizer(out_file, dark_mode=False)
tokenizer = get_tokenizer()
for inst_i, entry in enumerate(data):
if inst_i > 100:
break
tokens = entry.get_tokens("input_ids")
# tokens = entry.get_tokens("input_ids")
prob1 = entry.get_vector("prob1")
prob2 = entry.get_vector("prob2")
real_loss1 = entry.get_vector("per_example_loss1")
real_loss2 = entry.get_vector("per_example_loss2")
masked_lm_positions = entry.get_vector("masked_lm_positions")
for i, loc in enumerate(masked_lm_positions):
tokens[loc] = "[{}:{}]".format(i, tokens[loc])
html_writer.multirow_print(data.cells_from_tokens(tokens))
row2 = [Cell("prob1:")] + data.cells_from_anything(prob1)
row3 = [Cell("prob2:")] + data.cells_from_anything(prob2)
row4 = [Cell("real_loss1:")] + data.cells_from_anything(real_loss1)
row5 = [Cell("real_loss2:")] + data.cells_from_anything(real_loss2)
html_writer.multirow_print_from_cells_list([row2, row3, row4, row5])
def view_grad_overlap_per_mask():
filename = "ukp_lm_probs.pickle"
out_name = filename.split(".")[0] + ".html"
html_writer = HtmlVisualizer(out_name, dark_mode=False)
data = EstimatorPredictionViewerGosford(filename)
tokenizer = data.tokenizer
for inst_i, entry in enumerate(data):
tokens = entry.get_mask_resolved_input_mask_with_input()
highlight = lmap(is_mask, tokens)
scores = entry.get_vector("overlap_score")
pos_list = entry.get_vector("masked_lm_positions")
probs = entry.get_vector("masked_lm_log_probs")
probs = np.reshape(probs, [20, -1])
rows = []
for score, position, prob in zip(scores, pos_list, probs):
tokens[position] = "{}-".format(position) + tokens[position]
row = [Cell(position), Cell(score)]
for idx in np.argsort(prob)[::-1][:5]:
term = tokenizer.inv_vocab[idx]
p = math.exp(prob[idx])
row.append(Cell(term))
row.append(Cell(p))
rows.append(row)
cells = data.cells_from_tokens(tokens, highlight)
for score, position in zip(scores, pos_list):
cells[position].highlight_score = score / 10000 * 255
html_writer.multirow_print(cells, 20)
html_writer.write_table(rows)
def run():
tokenizer = get_tokenizer()
spr = StreamPickleReader("contradiction_prediction")
html = HtmlVisualizer("contradiction_prediction.html")
cnt = 0
while spr.has_next():
item = spr.get_item()
e, p = item
input_ids, _, _ = e
logit, explain = p
tokens = tokenizer.convert_ids_to_tokens(input_ids)
p, h = split_p_h_with_input_ids(tokens, input_ids)
p_score, h_score = split_p_h_with_input_ids(explain, input_ids)
p_score = normalize(p_score)
h_score = normalize(h_score)
p_cells = [Cell("P:")] + cells_from_tokens(p, p_score)
h_cells = [Cell("H:")] + cells_from_tokens(h, h_score)
html.write_paragraph(str(logit))
html.multirow_print(p_cells)
html.multirow_print(h_cells)
if cnt > 100:
break
cnt += 1
def per_doc_score():
filename = "tlm_view.pickle"
html_writer = HtmlVisualizer("per_doc_score.html", dark_mode=False)
data = EstimatorPredictionViewerGosford(filename)
amp = 20
small_threshold = 40
for inst_i, entry in enumerate(data):
if inst_i > 1000:
break
scores = entry.get_vector("priority_score")
tokens = entry.get_mask_resolved_input_mask_with_input()
cells = data.cells_from_tokens(tokens)
if len(cells) < small_threshold:
continue
avg_score = average(scores)
if -0.11 > avg_score > -0.30:
continue
print(average(scores))
html_writer.write_headline(avg_score)
rows = []
row = []
for idx, cell in enumerate(cells):
row.append(cell)
if len(row) == 20:
html_writer.write_table([row])
row = []
def show(out_file_name, summarized_table: List[Entry]):
html = HtmlVisualizer(out_file_name)
tokenizer = get_tokenizer()
num_print = 0
for input_ids, prob, contributions in summarized_table:
tokens = tokenizer.convert_ids_to_tokens(input_ids)
html.write_paragraph("Score : {}".format(prob))
cells = []
max_change = 0
for idx in range(len(input_ids)):
token = tokens[idx]
if token == "[PAD]":
break
if idx in contributions:
raw_score = contributions[idx]
max_change = max(abs(raw_score), max_change)
score = abs(raw_score) * 100
color = "R" if raw_score > 0 else "B"
c = Cell(token, highlight_score=score, target_color=color)
else:
c = Cell(token, highlight_score=150, target_color="Gray")
cells.append(c)
if max_change < 0.05:
pass
else:
html.multirow_print(cells, 30)
num_print += 1
print("printed {} of {}".format(num_print, len(summarized_table)))
def show_tfrecord(file_path):
itr = load_record_v2(file_path)
tokenizer = get_tokenizer()
name = os.path.basename(file_path)
html = HtmlVisualizer(name + ".html")
for features in itr:
input_ids = take(features["input_ids"])
alt_emb_mask = take(features["alt_emb_mask"])
tokens = tokenizer.convert_ids_to_tokens(input_ids)
p_tokens, h_tokens = split_p_h_with_input_ids(tokens, input_ids)
p_mask, h_mask = split_p_h_with_input_ids(alt_emb_mask, input_ids)
p_cells = [
Cell(p_tokens[i], 100 if p_mask[i] else 0)
for i in range(len(p_tokens))
]
h_cells = [
Cell(h_tokens[i], 100 if h_mask[i] else 0)
for i in range(len(h_tokens))
]
label = take(features["label_ids"])[0]
html.write_paragraph("Label : {}".format(label))
html.write_table([p_cells])
html.write_table([h_cells])
def main():
html = HtmlVisualizer("tf_rel_filter.html")
tokenizer = get_tokenizer()
path = "/mnt/nfs/work3/youngwookim/data/bert_tf/tf_rel_filter_B_dev/"
def itr():
for file in get_dir_files(path):
for item in load_record(file):
yield item
for feature in itr():
write_feature_to_html(feature, html, tokenizer)
def main():
save_name = "alamri_mismatch_all"
output_d = load_from_pickle(save_name)
html = HtmlVisualizer("alamri_mismatch.html")
tokenizer = get_tokenizer()
logits_grouped_by_layer = output_d["per_layer_logits"]
num_layers = 12
def float_arr_to_cell(head, float_arr):
return [Cell(head)] + lmap(Cell, map(two_digit_float, float_arr))
def float_arr_to_cell2(head, float_arr):
return [Cell(head)] + lmap(Cell, map("{0:.4f}".format, float_arr))
num_data = len(output_d['input_ids'])
for data_idx in range(num_data)[:100]:
def get(name):
return output_d[name][data_idx]
tokens = tokenizer.convert_ids_to_tokens(get("input_ids"))
ex_scores = get('ex_scores')
probs = scipy.special.softmax(get('logits'))
pred_str = make_prediction_summary_str(probs)
html.write_paragraph("Prediction: {}".format(pred_str))
html.write_paragraph("gold label={}".format(get("label")))
row1 = [Cell("")] + list(
[Cell(t, int(s * 100)) for t, s in zip(tokens, ex_scores)])
row2 = float_arr_to_cell("ex_prob", ex_scores)
for i, s in enumerate(ex_scores):
if s > 0.5:
row2[i + 1].highlight_score = 100
rows = [row1, row2]
for layer_no in range(num_layers):
layer_logit = logits_grouped_by_layer[layer_no][data_idx]
probs = sigmoid(layer_logit)
row = float_arr_to_cell("layer_{}".format(layer_no), probs[:, 1])
rows.append(row)
html.write_table(rows)
def per_doc_score():
filename = "fetch_hidden_dim.pickle"
html_writer = HtmlVisualizer("preserved.html", dark_mode=False)
p = os.path.join(output_path, filename)
raw_data = pickle.load(open(p, "rb"))
n_skip = 0
data = EstimatorPredictionViewerGosford(filename)
for inst_i, entry in enumerate(data):
if inst_i > 100:
break
count_preserved = entry.get_vector("layer_count")
tokens = entry.get_tokens("input_ids")
cells = data.cells_from_tokens(tokens)
valid_parst = count_preserved[:len(cells)]
avg = np.average(count_preserved)
row = []
row2 = []
#f_print = avg > 20
f_print = True
print(avg)
if f_print:
html_writer.write_paragraph("Skipped {} articles".format(n_skip))
n_skip = 0
for idx, cell in enumerate(cells):
score = count_preserved[idx] / 728 * 100
cell.highlight_score = score
row.append(cell)
row2.append((Cell(count_preserved[idx], score)))
if len(row) == 20:
html_writer.write_table([row, row2])
row = []
row2 = []
html_writer.write_paragraph(str(avg))
else:
n_skip += 1
def print_as_html(fn):
examples = load_record(fn)
tokenizer = tokenizer_wo_tf.FullTokenizer(
os.path.join(data_path, "bert_voca.txt"))
html_output = HtmlVisualizer("out_name.html")
for feature in examples:
masked_inputs = feature["input_ids"].int64_list.value
idx = 0
step = 512
while idx < len(masked_inputs):
slice = masked_inputs[idx:idx + step]
tokens = tokenizer.convert_ids_to_tokens(slice)
idx += step
cells = cells_from_tokens(tokens)
html_output.multirow_print(cells)
html_output.write_paragraph("----------")
def show_prediction(filename, file_path, correctness_1, correctness_2):
data = EstimatorPredictionViewerGosford(filename)
itr = load_record_v2(file_path)
tokenizer = get_tokenizer()
name = os.path.basename(filename)
html = HtmlVisualizer(name + ".html")
idx = 0
for entry in data:
features = itr.__next__()
input_ids = entry.get_vector("input_ids")
input_ids2 = take(features["input_ids"])
assert np.all(input_ids == input_ids2)
alt_emb_mask = take(features["alt_emb_mask"])
tokens = tokenizer.convert_ids_to_tokens(input_ids)
p_tokens, h_tokens = split_p_h_with_input_ids(tokens, input_ids)
p_mask, h_mask = split_p_h_with_input_ids(alt_emb_mask, input_ids)
p_cells = [
Cell(p_tokens[i], 100 if p_mask[i] else 0)
for i in range(len(p_tokens))
]
h_cells = [
Cell(h_tokens[i], 100 if h_mask[i] else 0)
for i in range(len(h_tokens))
]
label = take(features["label_ids"])[0]
logits = entry.get_vector("logits")
pred = np.argmax(logits)
if not correctness_1[idx] or not correctness_2[idx]:
html.write_paragraph("Label : {} Correct: {}/{}".format(
label, correctness_1[idx], correctness_2[idx]))
html.write_table([p_cells])
html.write_table([h_cells])
idx += 1
def loss_view():
filename = "sero_pred.pickle"
p = os.path.join(output_path, filename)
data = pickle.load(open(p, "rb"))
print(data[0]["masked_lm_example_loss"].shape)
print(data[0]["masked_input_ids"].shape)
html_writer = HtmlVisualizer("sero_pred.html", dark_mode=False)
data = EstimatorPredictionViewerGosford(filename)
for inst_i, entry in enumerate(data):
losses = entry.get_vector("masked_lm_example_loss")
print(losses)
tokens = entry.get_tokens("masked_input_ids")
cells = data.cells_from_tokens(tokens)
row = []
for idx, cell in enumerate(cells):
row.append(cell)
if len(row) == 20:
html_writer.write_table([row])
row = []
html_writer.multirow_print(data.cells_from_anything(losses), 20)
